SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data

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dc.citation.firstpage1847en_US
dc.citation.issueNumber11en_US
dc.citation.journalTitleGenome Researchen_US
dc.citation.lastpage1859en_US
dc.citation.volumeNumber29en_US
dc.contributor.authorZafar, Hamimen_US
dc.contributor.authorNavin, Nicholasen_US
dc.contributor.authorChen, Kenen_US
dc.contributor.authorNakhleh, Luayen_US
dc.date.accessioned2021-10-19T15:35:26Zen_US
dc.date.available2021-10-19T15:35:26Zen_US
dc.date.issued2019en_US
dc.description.abstractAccumulation and selection of somatic mutations in a Darwinian framework result in intra-tumor heterogeneity (ITH) that poses significant challenges to the diagnosis and clinical therapy of cancer. Identification of the tumor cell populations (clones) and reconstruction of their evolutionary relationship can elucidate this heterogeneity. Recently developed single-cell DNA sequencing (SCS) technologies promise to resolve ITH to a single-cell level. However, technical errors in SCS data sets, including false-positives (FP) and false-negatives (FN) due to allelic dropout, and cell doublets, significantly complicate these tasks. Here, we propose a nonparametric Bayesian method that reconstructs the clonal populations as clusters of single cells, genotypes of each clone, and the evolutionary relationship between the clones. It employs a tree-structured Chinese restaurant process as the prior on the number and composition of clonal populations. The evolution of the clonal populations is modeled by a clonal phylogeny and a finite-site model of evolution to account for potential mutation recurrence and losses. We probabilistically account for FP and FN errors, and cell doublets are modeled by employing a Beta-binomial distribution. We develop a Gibbs sampling algorithm comprising partial reversible-jump and partial Metropolis-Hastings updates to explore the joint posterior space of all parameters. The performance of our method on synthetic and experimental data sets suggests that joint reconstruction of tumor clones and clonal phylogeny under a finite-site model of evolution leads to more accurate inferences. Our method is the first to enable this joint reconstruction in a fully Bayesian framework, thus providing measures of support of the inferences it makes.en_US
dc.identifier.citationZafar, Hamim, Navin, Nicholas, Chen, Ken, et al.. "SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data." <i>Genome Research,</i> 29, no. 11 (2019) Cold Spring Harbor Laboratory Press: 1847-1859. https://doi.org/10.1101/gr.243121.118.en_US
dc.identifier.digitalZafar-1847-59en_US
dc.identifier.doihttps://doi.org/10.1101/gr.243121.118en_US
dc.identifier.urihttps://hdl.handle.net/1911/111569en_US
dc.language.isoengen_US
dc.publisherCold Spring Harbor Laboratory Pressen_US
dc.rightsThis article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/.en_US
dc.titleSiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing dataen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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